Traditionally, it was thought that two stage detectors, such as Faster R-CNN [1]}, were slower but provided a higher accuracy. However, Lin et al. [2]} investigated the reason of such behaviour and why Single Stage Detectors (SSD) performed worse. SSD detectors are very sensitive to the selection of anchor size and how such anchors cover the space of possible objects in the image. The conclusion of their investigation showed that SSDs are exposed to extreme class imbalance between foreground and background labels, where the latter case have much more examples than the earlier. In this situation, CNNs tend to be biased to predict the class with higher number of examples. This is not the case for R-CNN detectors, where the RPN reduces significantly the number of background samples but selecting a fixed number of samples, leading to a fixed foreground to background ratio of 1:3. On the other hand, SSDs need to evaluated a large number of candidate objects.
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